Mario Garcia Valdez | Instituto Tecnológico de Tijuana (original) (raw)

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Papers by Mario Garcia Valdez

Research paper thumbnail of Using Fuzzy Inference Systems for the Creation of Forex Market Predictive Models

IEEE Access, 2021

This paper presents a method for creating Forex market predictive models using multi-agent and fu... more This paper presents a method for creating Forex market predictive models using multi-agent and fuzzy systems, which have the objective of simulating the interactions that provoke changes in the price. Agents in the system represent traders performing buy and sell orders in a market, and fuzzy systems are used to model the rules followed by traders performing trades in a live market and intuitionistic fuzzy logic to model their decisions' indeterminacy. We use functions to restrict the agents' decisions, which make the agents become specialized at particular market conditions. These ''specialization'' functions use the grades of membership obtained from an agent's fuzzy system and thresholds obtained from training data sets, to determine if that agent is specialized enough to handle a market's current conditions. We have performed experiments and compared against the state of the art. Results demonstrate that our method obtains predictive errors (using mean absolute error) that are in the same order of magnitude than those errors obtained by models generated using deep learning and models generated by random forest, AdaBoost, XGBoost, and support-vector machines. Furthermore, we performed experiments that show that identifying specialized agents yields better results. INDEX TERMS Economic forecasting, fuzzy systems, multi-agent system, forex market.

Research paper thumbnail of Optimal design of type-2 and type-1 fuzzy tracking controllers for autonomous mobile robots under perturbed torques using a new chemical optimization paradigm

Expert Systems with Applications, 2013

ABSTRACT This paper addresses the tracking problem for the dynamic model of a unicycle mobile rob... more ABSTRACT This paper addresses the tracking problem for the dynamic model of a unicycle mobile robot. A novel optimization method inspired on the chemical reactions is applied to solve this motion problem by integrating a kinematic and a torque controller based on fuzzy logic theory. Computer simulations are presented confirming that this optimization paradigm is able to outperform other optimization techniques applied to this particular robot application.

Research paper thumbnail of Accelerometer-Based Hand Gesture Recognition Using Artificial Neural Networks

In this paper a hand gesture recognition method using Artificial Neural Networks (ANN) is present... more In this paper a hand gesture recognition method using Artificial Neural Networks (ANN) is presented, to evaluate this approach the three-axis accelerometer found in the Wiimote controller was used to generate a dataset of hand gestures of certain geometric shapes and letters. The gesture recognition process and its evaluation are discussed.

Research paper thumbnail of A comparative study of machine learning techniques in blog comments spam filtering

In this paper we compare four machine learning techniques for blog comments spam filtering. the m... more In this paper we compare four machine learning techniques for blog comments spam filtering. the machine learning techniques are the Naïve Bayes, K-nearest neighbor, neural networks and the support vector machines. For this comparative study we used a blog comment corpus that has been affected by spam, which is our study case in this work. We classify the comments of this blog comments corpus, which have 50 pages and 1024 blog comments are classified in spam an non-spam. The percentage of spam of this corpus is 67%.

Research paper thumbnail of A Fuzzy Approach for the Sequencing of Didactic Resources in Educational Adaptive Hypermedia Systems

Research in educational adaptive hypermedia systems has been concerned with the generation of per... more Research in educational adaptive hypermedia systems has been concerned with the generation of personalized courses, in this work we focus on a task of these kind of systems: the semi-automatic sequencing of didactic resources. Sequencing defines the order in which topics (and didactic resources) in a course will be presented to learners, considering for this, their previous knowledge and particular objectives. This task is based on subjective information, for example the learner knowledge, preferences, learning style, and even assessment results are perceived differently depending the context. In this paper we define an architecture for sequencing of didactic materials using fuzzy attributes and rules.

Research paper thumbnail of Design and Implementation of an Inference Engine for Fuzzy Systems

This paper describes the design and implement ation of an inference engine for the execution of F... more This paper describes the design and implement ation of an inference engine for the execution of Fuzzy Inference Systems (FIS), the architecture of the system is presented, and the object oriented design of the main modules is also discussed. The engine is implement ed as a component to be referenced by other applications locally or remotely as a web service. This engine is needed by our research group for the implement ation of other projects, which are Internet and Web based. The distinctive characteristic of this component is the ability to define fuzzy objects and attributes.

Research paper thumbnail of A Hybrid Recommender System Architecture for Learning Objects

In this paper we present the architecture of a hybrid recommender system to support an adaptive h... more In this paper we present the architecture of a hybrid recommender system to support an adaptive hypermedia educational (AHE) system. Currently the instructor (using fuzzy rules) specifies the sequence in which learning objects are presented to students. The instructor can also give students a chance to choose from a pool of objects and helps them make their selection by assigning to each object a recommendation rating based on the student’s profile. We propose a hybrid recommender system that uses collaborative filtering techniques together with fuzzy inference systems to provide recommendations, considering the instructor’s experience as well as the ratings given by similar students.

Research paper thumbnail of Dynamic Fuzzy Logic Parameter Tuning

Ant Colony Optimization (ACO) is a population-based constructive meta-heuristic that exploits a f... more Ant Colony Optimization (ACO) is a population-based constructive meta-heuristic that exploits a form of past performance memory inspired by the foraging behaviour of real ants. The behaviour of the ACO algorithm is highly dependent on the values defined for its parameters.

Research paper thumbnail of Using Fuzzy Inference Systems for the Creation of Forex Market Predictive Models

IEEE Access, 2021

This paper presents a method for creating Forex market predictive models using multi-agent and fu... more This paper presents a method for creating Forex market predictive models using multi-agent and fuzzy systems, which have the objective of simulating the interactions that provoke changes in the price. Agents in the system represent traders performing buy and sell orders in a market, and fuzzy systems are used to model the rules followed by traders performing trades in a live market and intuitionistic fuzzy logic to model their decisions' indeterminacy. We use functions to restrict the agents' decisions, which make the agents become specialized at particular market conditions. These ''specialization'' functions use the grades of membership obtained from an agent's fuzzy system and thresholds obtained from training data sets, to determine if that agent is specialized enough to handle a market's current conditions. We have performed experiments and compared against the state of the art. Results demonstrate that our method obtains predictive errors (using mean absolute error) that are in the same order of magnitude than those errors obtained by models generated using deep learning and models generated by random forest, AdaBoost, XGBoost, and support-vector machines. Furthermore, we performed experiments that show that identifying specialized agents yields better results. INDEX TERMS Economic forecasting, fuzzy systems, multi-agent system, forex market.

Research paper thumbnail of Optimal design of type-2 and type-1 fuzzy tracking controllers for autonomous mobile robots under perturbed torques using a new chemical optimization paradigm

Expert Systems with Applications, 2013

ABSTRACT This paper addresses the tracking problem for the dynamic model of a unicycle mobile rob... more ABSTRACT This paper addresses the tracking problem for the dynamic model of a unicycle mobile robot. A novel optimization method inspired on the chemical reactions is applied to solve this motion problem by integrating a kinematic and a torque controller based on fuzzy logic theory. Computer simulations are presented confirming that this optimization paradigm is able to outperform other optimization techniques applied to this particular robot application.

Research paper thumbnail of Accelerometer-Based Hand Gesture Recognition Using Artificial Neural Networks

In this paper a hand gesture recognition method using Artificial Neural Networks (ANN) is present... more In this paper a hand gesture recognition method using Artificial Neural Networks (ANN) is presented, to evaluate this approach the three-axis accelerometer found in the Wiimote controller was used to generate a dataset of hand gestures of certain geometric shapes and letters. The gesture recognition process and its evaluation are discussed.

Research paper thumbnail of A comparative study of machine learning techniques in blog comments spam filtering

In this paper we compare four machine learning techniques for blog comments spam filtering. the m... more In this paper we compare four machine learning techniques for blog comments spam filtering. the machine learning techniques are the Naïve Bayes, K-nearest neighbor, neural networks and the support vector machines. For this comparative study we used a blog comment corpus that has been affected by spam, which is our study case in this work. We classify the comments of this blog comments corpus, which have 50 pages and 1024 blog comments are classified in spam an non-spam. The percentage of spam of this corpus is 67%.

Research paper thumbnail of A Fuzzy Approach for the Sequencing of Didactic Resources in Educational Adaptive Hypermedia Systems

Research in educational adaptive hypermedia systems has been concerned with the generation of per... more Research in educational adaptive hypermedia systems has been concerned with the generation of personalized courses, in this work we focus on a task of these kind of systems: the semi-automatic sequencing of didactic resources. Sequencing defines the order in which topics (and didactic resources) in a course will be presented to learners, considering for this, their previous knowledge and particular objectives. This task is based on subjective information, for example the learner knowledge, preferences, learning style, and even assessment results are perceived differently depending the context. In this paper we define an architecture for sequencing of didactic materials using fuzzy attributes and rules.

Research paper thumbnail of Design and Implementation of an Inference Engine for Fuzzy Systems

This paper describes the design and implement ation of an inference engine for the execution of F... more This paper describes the design and implement ation of an inference engine for the execution of Fuzzy Inference Systems (FIS), the architecture of the system is presented, and the object oriented design of the main modules is also discussed. The engine is implement ed as a component to be referenced by other applications locally or remotely as a web service. This engine is needed by our research group for the implement ation of other projects, which are Internet and Web based. The distinctive characteristic of this component is the ability to define fuzzy objects and attributes.

Research paper thumbnail of A Hybrid Recommender System Architecture for Learning Objects

In this paper we present the architecture of a hybrid recommender system to support an adaptive h... more In this paper we present the architecture of a hybrid recommender system to support an adaptive hypermedia educational (AHE) system. Currently the instructor (using fuzzy rules) specifies the sequence in which learning objects are presented to students. The instructor can also give students a chance to choose from a pool of objects and helps them make their selection by assigning to each object a recommendation rating based on the student’s profile. We propose a hybrid recommender system that uses collaborative filtering techniques together with fuzzy inference systems to provide recommendations, considering the instructor’s experience as well as the ratings given by similar students.

Research paper thumbnail of Dynamic Fuzzy Logic Parameter Tuning

Ant Colony Optimization (ACO) is a population-based constructive meta-heuristic that exploits a f... more Ant Colony Optimization (ACO) is a population-based constructive meta-heuristic that exploits a form of past performance memory inspired by the foraging behaviour of real ants. The behaviour of the ACO algorithm is highly dependent on the values defined for its parameters.